Introduction

Research data management involves activities throughout the research data lifecycle; planning and making decisions about the collection, organisation, storage, preservation, publishing and sharing of research data.

There is no clear consensus on a definition of research data because the nature of it can vary widely depending on the subject discipline or research funder. But in any context, research data represent the accumulation of a significant amount of effort, time, and resources.

The University of Salford defines research data as ‘information created, observed or collected in the course of research which is necessary to support or validate a research project’s observations, findings or outputs’.

Within the arts, research data can be evidence of an identified research activity and can include preparatory, unfinished and supportive work.

Experimental: data from experimental results e.g. produced in a laboratory by applying a treatment or control condition and recording specific variables that result. The data is often reproducible but could be costly. Examples include clinical trials, gene sequences, chromatograms, microassays

Simulation: Data generated from test models, where model and metadata may be more important than output data from the model e.g. climate, mathematical or economic models

Reference: A (static or organic) conglomeration or collection of smaller (peer-reviewed) datasets, most probably published and curated. For example, gene sequence databanks, chemical structures, crystallographic databases, or spatial data portals.

Examples of research data:

Documents and spreadsheets

Questionnaires, surveys, transcripts

Audio and video tapes

Photographs/scans

Results of experiments or simulations

Database contents

Statistical data sets (e.g. SPSS, Stata, SAS)

Qualitative data sets (e.g. NVivo, ATLAS.ti, NUD*IST)

Software

Program source code

Models, algorithms, scripts

Methodologies and workflows

Patient records

Personal correspondence

Many researchers still record data in non-digital formats such as in laboratory notebooks, sketches, photographic film, prints and hand-written questionnaires. More information about managing non-digital data is available here.

The University of Bristol have collated a useful glossary of terms relating to research data management.

Research data management is vital for ensuring the sustainability, discoverability and accessibility of data in the long term. It enhances the integrity and efficiency of research and facilitates data sharing, validation and re-use in accordance with legal, ethical and funder requirements.